Sentiment Analysis Project

Welcome to the Sentiment Analysis Project! This repository contains scripts and data for performing sentiment analysis on tweets related to Cryptocurrencies on X (Formerly Twitter) . Below you'll find a detailed description of the project structure, its components, and how to use them.

Project Structure

The project is organized as follows:

Sentiment Analysis Project
│
├── CSV Files
│   ├── cleaned_tweets.csv
│   ├── training_dataset.csv
│   ├── testing_dataset.csv
│   └── extracted_tweets.csv
│
├── label_tweets.py
├── dataset_splitter.py
├── naive_bayes_classifier.py
├── random_forest.py
├── support_vector_machine.py
├── testing_svm.py
├── twitter_scraping.py
├── twitter_sentiment_analysis_app.py
└── svm_classifier.pkl

CSV Files

• cleaned_tweets.csv: Cleaned dataset with additional sentiment analysis columns.
• training_dataset.csv: Training dataset for machine learning models.
• testing_dataset.csv: Testing dataset for evaluating model performance.
• new_dataset_with_predictions.csv: Dataset with predicted sentiments from the SVM model.

Scripts

• label_tweets.py: Cleans and labels tweets based on their sentiment using TextBlob.
• dataset_splitter.py: Splits the cleaned dataset into training and testing sets.
• naive_bayes_classifier.py: Trains and evaluates a Naive Bayes classifier for sentiment analysis.
• random_forest.py: Trains and evaluates a Random Forest classifier for sentiment analysis.
• support_vector_machine.py: Trains and evaluates a Support Vector Machine (SVM) classifier for sentiment analysis.
• testing_svm.py: Tests the SVM model on a new dataset and evaluates its performance.
• twitter_scraping.py: Scrapes tweets from Twitter using Selenium and saves them to a CSV file.
• twitter_sentiment_analysis_app.py: A Streamlit web application for scraping tweets, cleaning data, performing sentiment analysis, and displaying results.

Model Files

• svm_classifier.pkl: Trained SVM classifier model.

Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.x
• Pandas
• NumPy
• Scikit-learn
• TextBlob
• Selenium
• TQDM
• Joblib
• Streamlit
• ChromeDriver

Installation

Clone the repository and navigate to the project directory:

git clone https://github.com/yourusername/Sentiment-Analysis-Project.git
cd Sentiment-Analysis-Project

Install the required Python packages:

pip install -r requirements.txt

Usage

1. Scrape Tweets: Run the twitter_scraping.py script to scrape tweets about Bitcoin and save them to Bitcoin_tweets.csv.

   python twitter_scraping.py

2. Label Tweets: Run the label_tweets.py script to clean and label the tweets.

   python label_tweets.py

3. Split Dataset: Run the dataset_splitter.py script to split the cleaned dataset into training and testing sets.

   python dataset_splitter.py

4. Train Classifiers: You can train different classifiers using the provided scripts:

   • Naive Bayes Classifier:

     python naive_bayes_classifier.py

   • Random Forest Classifier:

     python random_forest.py

   • Support Vector Machine (SVM) Classifier:

     python support_vector_machine.py

5. Test SVM Model: Run the testing_svm.py script to test the SVM model on a new dataset.

   python testing_svm.py

6. Streamlit Application: Launch the Streamlit web application for scraping tweets, performing sentiment analysis, and displaying results.

   streamlit run twitter_sentiment_analysis_app.py

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.

Acknowledgments

• The TextBlob library for sentiment analysis.
• The Selenium library for web scraping.
• The Scikit-learn library for machine learning algorithms.
• The Streamlit library for building the web application.

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